The Impacts of Stop & Frisk

Evidence From The Housing Market in New York City

Alex Cardazzi

Old Dominion University

Background & Timeline

  • Starting in 1994, “Stop, Question & Frisk” is a strategy resulting from “Broken Window Policing”
  • On January 31st, 2008, a federal class action lawsuit claimed Stop & Frisk “encourages racial profiling and unconstitutional detainment”
  • On May 16th, 2012, a judge granted class certification for the lawsuit.
  • The trial began on March 18th, 2013 and ends on August 12th, 2013 with a judge ruling Stop & Frisk to be unconstitutional.
  • Stop & Frisk still occurs in New York City today.

Background & Timeline

via The Wall Street Journal

Background & Timeline

Link to Google Trends

Background & Timeline

The Police Department has failed to bring down crime. Perpetrators of violent crime in New York City are concentrated among relatively small groups in areas with high rates of crime. The Police Department has sophisticated databases to help it understand who they are. It has to make more effective use of “precision policing,” including the narrow and tactical use of “stop and frisk” to focus its resources on those who are thought to be the primary drivers of violence.

NYT (2024)

Background & Timeline

Background & Timeline

Literature Review

Literature Review

Crime (and risk of crime) reduces housing prices

Tita, Petras, and Greenbaum (2006); Ihlanfeldt and Mayock (2010); Linden and Rockoff (2008); Pope (2008); Caudill, Affuso, and Yang (2015); Kim and Lee (2018)

Stop & Frisk marginally reduces crime
MacDonald, Fagan, and Geller (2016); Bacher-Hicks and Campa (2021b)

Literature Review

Other impacts of Stop & Frisk:
Decreased educational outcomes (Legewie and Fagan 2019; Bacher-Hicks and Campa 2021a)
Decreased views of police legitimacy (Tyler, Fagan, and Geller 2014)
Decreased mental health (Geller et al. 2014)
Housing Prices:
Friedman (2015) shows that properties exposed to more intense Stop & Frisk behavior sell for lower prices
Each additional stop decreases prices by $35 - $250
Using data from 2006 - 2012 (includes recession & avoids court case)
Does not control for crime

Research Question

How do we value the way we are policed?

  • Homeowner distaste for police hostility, or the “reminder” of crime, may reduce housing prices (especially if the crime benefits are minimal).

  • Even if Stop & Frisk may do little to prevent crime, it could give off the perception of safety.

  • This is an important, timely question as cities contemplate different policing strategies in the wake of violence, riots, etc.

Data & Methodology

Operation Impact

MacDonald, Fagan, and Geller (2016)

In January 2003, the NYPD deployed roughly two-thirds of its police academy graduates—about 1,500 new police officers—to Impact Zones.

  • Police Commanders nominated crime “hot spots” within their precincts
  • Every 6 months, these zones would be re-evaluated and adjusted.

Operation Impact

Operation Impact

Operation Impact

Data

  1. New York City (Residential) Property Sales
    • NYC Dept. of Finance
    • Automated City Register Information System (ACRIS)
  2. New York Police Department (NYPD)
    • NYPD Operation Impact Zones\(^{*}\)
    • NYPD Stop & Frisk and Crime Data
  3. American Community Survey Data for New York City

Data

Number of Transactions

Methodology

I want to know how property prices changes after Stop & Frisk was ruled unconstitutional.

Idea 1: Calculate Average Prices Before & After

This won’t work because there might be price dynamics going on in NYC that occur at the same time as the Floyd case. How can I be sure I am not capturing those effects?

Methodology

I want to know how property prices changes after Stop & Frisk was ruled unconstitutional.

Idea 1: Calculate Average Prices Before & After

This won’t work because there might be price dynamics going on in NYC that occur at the same time as the Floyd case. How can I be sure I am not capturing those effects?

Idea 2: Calculate Difference in Prices between Treatment and Control

This won’t work because there might be pre-existing differences between treatment and control. How can I be sure I am not capturing those effects?

Methodology

I want to know how property prices changes after Stop & Frisk was ruled unconstitutional.

Idea 1: Calculate Average Prices Before & After

This won’t work because there might be price dynamics going on in NYC that occur at the same time as the Floyd case. How can I be sure I am not capturing those effects?

Idea 2: Calculate Difference in Prices between Treatment and Control

This won’t work because there might be pre-existing differences between treatment and control. How can I be sure I am not capturing those effects?

Idea 3: Combine Ideas 1 and 2

Calculate difference (before and after) for both treatment and control. Whatever happened in the control should have also happened in the treatment. Subtracting the change in the control from the change in the treatment will isolate the treatment effect.

Methodology

I want to know how property prices changes after Stop & Frisk was ruled unconstitutional.

Idea 1: Calculate Average Prices Before & After

This won’t work because there might be price dynamics going on in NYC that occur at the same time as the Floyd case. How can I be sure I am not capturing those effects?

Idea 2: Calculate Difference in Prices between Treatment and Control

This won’t work because there might be pre-existing differences between treatment and control. How can I be sure I am not capturing those effects?

Idea 3: Combine Ideas 1 and 2

Calculate difference (before and after) for both treatment and control. Whatever happened in the control should have also happened in the treatment. Subtracting the change in the control from the change in the treatment will isolate the treatment effect. This is called difference-in-differences.

Econometrics

Empirical Model

\[\begin{align}\text{log}(P_{it}) =& \ X_{it}'\beta + \delta_1 \text{Floyd}_{t} + \delta_2 \text{Impact}_{i} \\ &+ \delta_3 (\text{Floyd}_{t} \times \text{Impact}_{i}) + \epsilon_{it}\end{align}\]

where:
\(X_{it}'\) are variables specific to the property or neighborhood.
Floyd\(_{t}\) is an indicator equal to 1 for periods between trial dates.
Impact\(_i\) is an indicator equal to 1 for properties inside Impact Zones.

 

\(\delta_3\) is the “treatment effect”: how much more did properties prices change in Impact Zones, relative to nearby properties, when Stop & Frisk was ruled unconstitutional.

If property prices for both areas changed in similar ways, \(\delta_3 \approx 0\).

Time Series

Event Study

Results

Repeat Sales

Distance from Border

Distance from Border

Replicating Friedman (2015)

We replicate and “extend” the analysis in Friedman (2015):

Friedman (2015):
Sample Years: Jan 2007 - Dec 2012
Sample Size: 113,007 vs 113,765 (this analysis)
Main result: -$55.04/stop vs -$61.68/stop (this analysis)

Replicating Friedman (2015)

Conclusion

Conclusion

  1. Contrary to Friedman (2015), the intensive margin of Stop & Frisk itself does not appear to influence housing prices
  2. Values were previously depressed in areas subjected to Operation Impact before Stop & Frisk was ruled to be illegal
  3. Resolving uncertainty about the future of Stop & Frisk led to prices “catching up”

alexcardazzi.github.io

Bibliography

Bacher-Hicks, Andrew, and Elijah de la Campa. 2021a. “Social costs of proactive policing: The impact of NYC’s Stop and Frisk program on educational attainment.” Working Paper.
———. 2021b. “The impact of New York City’s Stop and Frisk program on crime: The case of police commanders.” Working Paper.
Caudill, Steven B, Ermanno Affuso, and Ming Yang. 2015. “Registered sex offenders and house prices: An hedonic analysis.” Urban Studies 52 (13): 2425–40.
Friedman, Matthew. 2015. “Valuing proactive policing: A hedonic analysis of Stop & Frisk’s amenity value.” Available at SSRN 2695584.
Geller, Amanda, Jeffrey Fagan, Tom Tyler, and Bruce G Link. 2014. “Aggressive policing and the mental health of young urban men.” American Journal of Public Health 104 (12): 2321–27.
Ihlanfeldt, Keith, and Tom Mayock. 2010. “Panel data estimates of the effects of different types of crime on housing prices.” Regional Science and Urban Economics 40 (2-3): 161–72.
Kim, Seonghoon, and Kwan Ok Lee. 2018. “Potential crime risk and housing market responses.” Journal of Urban Economics 108: 1–17.
Legewie, Joscha, and Jeffrey Fagan. 2019. “Aggressive Policing and the Educational Performance of Minority Youth.” American Sociological Review 84 (2): 220–47.
Linden, Leigh, and Jonah E Rockoff. 2008. “Estimates of the Impact of Crime Risk on Property Values from Megan’s Laws.” American Economic Review 98 (3): 1103–27.
MacDonald, John, Jeffrey Fagan, and Amanda Geller. 2016. “The Effects of Local Police Surges on Crime and Arrests in New York City.” PLoS One 11 (6): e0157223.
Pope, Jaren C. 2008. “Fear of Crime and Housing Prices: Household Reactions to Sex Offender Registries.” Journal of Urban Economics 64 (3): 601–14.
Tita, George E, Tricia L Petras, and Robert T Greenbaum. 2006. “Crime and Residential Choice: A Neighborhood Level Analysis of the Impact of Crime on Housing Prices.” Journal of Quantitative Criminology 22 (4): 299.
Tyler, Tom R, Jeffrey Fagan, and Amanda Geller. 2014. “Street Stops and Police Legitimacy: Teachable Moments in Young Urban Men’s Legal Socialization.” Journal of Empirical Legal Studies 11 (4): 751–85.

Virginia Polytechnic Institute and State University